Connectionism and Information Processing Abstractions

Balakrishnan Chandrasekaran, Askhok Goel, Dean Allemang

Abstract

Connectionism challenges a basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipulations. Connectionism replaces symbol structures with distributed representations in the form of weights between units. For problems close to the architecture of the underlying machines, connectionist and symbolic approaches can make different representational commitments for a task and, thus, can constitute different theories. For complex problems, however, the power of a system comes more from the content of the representations than the medium in which the representations reside. The connectionist hope of using learning to obviate explicit specification of this content is undermined by the problem of programming appropriate initial connectionist architectures so that they can in fact learn. In essence, although connectionism is a useful corrective to the view of mind as a Turing machine, for most of the central issues of intelligence, connectionism is only marginally relevant.